Pseudo-Plane Regularized Signed Distance Field for Neural Indoor Scene Reconstruction

Academic Background

3D reconstruction of indoor scenes is a significant task in computer vision with broad applications, such as computer graphics and virtual reality. Traditional 3D reconstruction methods often rely on expensive 3D ground truth data. In recent years, neural implicit surface representation methods based on Neural Radiance Fields (NeRF) have demonstrated powerful 3D surface reconstruction capabilities using only multiple images. However, since NeRF primarily optimizes based on volumetric rendering of color, its reconstruction performance in low-textured regions (e.g., floors, walls) is usually poor. These low-textured regions are common in indoor scenes and often correspond to planar structures. Therefore, improving the reconstruction quality of low-textured regions without introducing additional supervisory signals or making additional assumptions about room layouts has become a pressing issue.

This paper proposes a novel indoor scene reconstruction method based on a pseudo-plane regularized signed distance field (PPlaneSDF). The method treats adjacent pixels with similar colors as belonging to the same pseudo-plane and dynamically estimates plane parameters during training, thereby regularizing the signed distance field of points on the planes. Additionally, the paper introduces a keypoint-guided ray sampling strategy to enhance training efficiency and improve reconstruction quality.

Source of the Paper

This paper is co-authored by Jing Li, Jinpeng Yu, Ruoyu Wang, and Shenghua Gao, affiliated with ShanghaiTech University, Xiaohongshu Technology Incorporated Company, and The University of Hong Kong, respectively. The paper was published in 2024 in the International Journal of Computer Vision.

Research Process and Results

1. Research Process

1.1 Pseudo-Plane Generation

The paper first clusters adjacent pixels with similar colors into pseudo-planes using super-pixel segmentation. These pseudo-planes include not only large areas such as walls and floors but also small planar regions on objects (e.g., the exterior of chairs and pianos). This unsupervised approach generates plane segments.

1.2 Pseudo-Plane Parameter Estimation

To dynamically estimate plane parameters during training, the paper proposes an efficient two-step strategy: - Step 1: Rough Plane Parameter Estimation
During rendering, a few points are sampled per plane segment, and their depths are obtained through volumetric rendering. The 3D coordinates of these points are fitted using the least squares method to obtain rough plane parameters. Due to the limited number of sampled points, the estimated plane parameters are noisy and inaccurate.

• Step 2: Rectified Plane Parameter Estimation
  More points are resampled on the roughly estimated plane, and their signed distances and normal directions are directly obtained by querying a multi-layer perceptron (MLP). Assuming these points are close enough to the true plane, the points are marched based on their signed distances and normal directions to obtain more accurate plane parameters.

1.3 Pseudo-Plane Regularization

After obtaining the rectified plane parameters, the signed distances of the sampled points are regularized to match the distances to the planes. This significantly improves the reconstruction quality in planar regions.

1.4 Plane Segment Fusion and Weighting

Since unsupervised plane segments are often noisy and inaccurate, the paper proposes a weighting strategy based on fusing plane segments from multiple views. By fusing segmentation results from different views, different weights are assigned to the sampled points, reducing the impact of noise during plane estimation and regularization.

1.5 Keypoint-Guided Ray Sampling Strategy

To avoid redundant ray sampling in planar regions, the paper introduces a keypoint-guided ray sampling strategy. By extracting keypoints from the image and increasing the sampling probability of rays around these keypoints, the network focuses more on textured regions, thereby improving reconstruction quality.

2. Research Results

Extensive experiments were conducted on the ScanNet and 7-Scenes datasets to validate the effectiveness and generalization ability of the proposed method. The results show that PPlaneSDF not only achieves competitive reconstruction performance in Manhattan scenes but also generalizes well to non-Manhattan scenes.

• Manhattan Scenes: In Manhattan scenes, PPlaneSDF significantly outperforms existing methods in reconstructing large planar regions such as walls and floors, especially in the details of small planar regions (e.g., furniture surfaces).

• Non-Manhattan Scenes: In non-Manhattan scenes, PPlaneSDF also performs well, handling complex scenes with multiple dominant directions, while existing methods (e.g., Manhattan-SDF) perform poorly due to their reliance on the Manhattan world assumption.

3. Conclusions and Significance

The proposed PPlaneSDF method significantly improves the quality of indoor scene reconstruction through pseudo-plane regularized signed distance fields. Its main contributions include: 1. A pseudo-plane-based regularization method that does not require additional geometric annotations or room layout assumptions. 2. An efficient two-step plane parameter estimation strategy that dynamically estimates plane parameters during training. 3. A weighting strategy based on multi-view plane segment fusion, reducing the impact of noise on reconstruction results. 4. A keypoint-guided ray sampling strategy that improves training efficiency and reconstruction quality.

The method not only performs well in Manhattan scenes but also generalizes effectively to non-Manhattan scenes, demonstrating its broad application potential in complex indoor scene reconstruction.

Research Highlights

1. Pseudo-Plane Regularization: By treating pixels with similar colors as pseudo-planes and dynamically estimating plane parameters during training, the method significantly improves reconstruction quality in low-textured regions.
2. Multi-View Plane Segment Fusion: By fusing plane segments from different views, the impact of noise on reconstruction results is reduced.
3. Keypoint-Guided Ray Sampling: By increasing the sampling probability of rays in textured regions, training efficiency and reconstruction details are improved.

Other Valuable Information

The paper also conducts extensive ablation experiments to validate the effectiveness of each module. The results show that pseudo-plane regularization, multi-view plane segment fusion, and keypoint-guided ray sampling all significantly contribute to the final reconstruction quality. Additionally, the paper demonstrates the combined effect of PPlaneSDF with existing methods (e.g., Manhattan-SDF), further improving reconstruction quality.

PPlaneSDF provides a new approach to 3D reconstruction of indoor scenes, showcasing its broad application potential in complex scenarios.